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          GeoHash系列 —— 一、基本介绍
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        <p>系列文章</p>
<p><a target="_blank" rel="noopener" href="http://historyzhang.github.io/2015/08/26/2015-08-26-geohash-xi-lie-zhi-yi-ji-ben-jie-shao/"><code>GeoHash</code>系列 —— 一、基本介绍</a></p>
<p><a target="_blank" rel="noopener" href="http://historyzhang.github.io/2015/08/27/2015-08-27-geohash-xi-lie-er-suan-fa-bu-zou/"><code>GeoHash</code>系列 —— 二、算法步骤</a></p>
<h1 id="引子"><a href="#引子" class="headerlink" title="引子"></a>引子</h1><p>目前很多的App都会使用到用户的地理位置信息，然后为用户提供相关服务，也就是我们所说的<code>LBS</code>。而<code>LBS</code>最基本的就是<code>附近</code>。所以就会有这样的一个问题：</p>
<p><code>什么样叫附近？</code></p>
<p>简单来说，附件就是以我为圆心，<code>radius</code>为半径画一个圆，圆内的都叫<code>附近</code>。</p>
<p>然而事实并没有这么简单。比如我要在地图上找附近的餐馆，不可能说把所有的餐馆和我的距离都算一遍，然后和<code>radius</code>比较。</p>
<p>稍微有点编程基础的应该都不会用这个办法。</p>
<p>其实那么庞大的数据，只有一小部分是我们想要的，这样我们通过某种方法来过滤掉不要的餐馆，减少计算的量，就避免了上述方法的弊端。</p>
<p>这样我们可以使用索引技术。</p>
<span id="more"></span>
<p>一提到索引，大家脑子里马上浮现出B树索引，因为大量的数据库（如MySQL、oracle、PostgreSQL等）都在使用B树。B树索引本质上是对索引字段进行排序，然后通过类似二分查找的方法进行快速查找，即它要求索引的字段是可排序的，一般而言，可排序的是一维字段，比如时间、年龄、薪水等等。但是对于空间上的一个点（二维，包括经度和纬度），如何排序呢？又如何索引呢？解决的方法很多，下文介绍一种方法来解决这一问题。</p>
<blockquote>
<p>思想：如果能通过某种方法将二维的点数据转换成一维的数据，那样不就可以继续使用B树索引了嘛。那这种方法真的存在嘛，答案是肯定的。目前很火的GeoHash算法就是运用了上述思想，下面我们就开始GeoHash之旅吧。</p>
</blockquote>
<h1 id="基本介绍"><a href="#基本介绍" class="headerlink" title="基本介绍"></a>基本介绍</h1><ul>
<li><code>GeoHash</code>将二维的经纬度转换成字符串，每个字符串代表某一矩形区域。这样，矩形内所有的点（经纬度）都共享相同的<code>GeoHash</code>字符串，这样就比较容易做缓存。例如下图展示了北京9个区域的<code>GeoHash</code>字符串。例如左上角这个区域内的用户不断发送位置信息请求餐馆数据，由于这些用户的GeoHash字符串都是WX4ER，所以可以把<code>WX4ER</code>当作<code>key</code>，把该区域的餐馆信息当作<code>value</code>来进行缓存，而如果不使用<code>GeoHash</code>的话，由于区域内的用户传来的经纬度是各不相同的，很难做缓存。</li>
</ul>
<img src="/images/blog/geohash_1.jpg" class="">

<ul>
<li><p>字符串越长，表示的范围越精确。5位的编码能表示10平方千米范围的矩形区域，而6位编码能表示更精细的区域（约0.34平方千米）。</p>
</li>
<li><p>字符串相似的表示距离相近（特殊情况后文阐述），这样可以利用字符串的前缀匹配来查询附近的POI信息。如下两个图所示，一个在城区，一个在郊区，城区的<code>GeoHash</code>字符串之间比较相似，郊区的字符串之间也比较相似，而城区和郊区的<code>GeoHash</code>字符串相似程度要低些。</p>
</li>
</ul>
<img src="/images/blog/geohash_2.jpg" class="">

<p>通过上面的介绍我们知道了<code>GeoHash</code>就是一种将经纬度转换成字符串的方法，并且使得在大部分情况下，字符串前缀匹配越多的距离越近，回到我们的案例，根据所在位置查询来查询附近餐馆时，只需要将所在位置经纬度转换成<code>GeoHash</code>字符串，并与各个餐馆的<code>GeoHash</code>字符串进行前缀匹配，匹配越多的距离越近。</p>
<h1 id="下集预告"><a href="#下集预告" class="headerlink" title="下集预告"></a>下集预告</h1><p><code>GeoHash</code>算法的步骤</p>

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